---
title: Vectorize
---

> [Vectorize](https://vectorize.io/) helps you build AI apps faster and with less hassle.
> It automates data extraction, finds the best vectorization strategy using RAG evaluation,
> and lets you quickly deploy real-time RAG pipelines for your unstructured data.
> Your vector search indexes stay up-to-date, and it integrates with your existing vector database,
> so you maintain full control of your data.
> Vectorize handles the heavy lifting, freeing you to focus on building robust AI solutions without getting bogged down by data management.

# Installation and Setup

Install the following Python package:

<CodeGroup>
```bash pip
pip install langchain-vectorize
```

```bash uv
uv add langchain-vectorize
```
</CodeGroup>

Sign up for a free Vectorize account [here](https://platform.vectorize.io/)
Generate an access token in the [Access Token](https://docs.vectorize.io/rag-pipelines/retrieval-endpoint#access-tokens) section
Gather your organization ID. From the browser url, extract the UUID from the URL after /organization/

Set up the following variables:
```python
VECTORIZE_ORG_ID="your-organization-id"
VECTORIZE_API_TOKEN="your-api-token"
```

## Retriever

```python
from langchain_vectorize import VectorizeRetriever

retriever = VectorizeRetriever(
    api_token=VECTORIZE_API_TOKEN,
    organization=VECTORIZE_ORG_ID,
    pipeline_id="...",
)
retriever.invoke("query")
```

Learn more in the [example notebook](/oss/integrations/retrievers/vectorize).
